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The paper is concerned with the application of wavelet-based neural networks for optimal control of robotic manipulators motion. Optimal control law is found by optimization of Hamilton-Jacobi-Bellman (H-J-B) equation and it is shown how wavelet-based neural networks can overcome nonlinearities through optimization with no preliminary off-line learning phase required. The neural network is learned...
It has been shown that any movement that satisfies the minimum joint angle jerk constraint can be reproduced by linear combination of 6 consistent Movement Elements (MEs). In this work, we assumed that any given movement is consisted of several successive phases (without overlap) such that each phase satisfies the minimum joint angle jerk constraint. Based on this assumption, we have developed a new...
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